Exploratory Data Analysis

Background

We’re going to explore a large data set or traffic crashes to learn about what factors are connected with injuries. We will use data from the city of Chicago’s open data portal. (This activity is derived from a blog post by Julia Silge)

years_ago <- mdy("01/01/2022") # data from last 2 years. May take time to load!
crash_url <- glue::glue("https://data.cityofchicago.org/Transportation/Traffic-Crashes-Crashes/85ca-t3if?$where=CRASH_DATE >= '{years_ago}'")
crash_raw <- as_tibble(read.socrata(crash_url)) # a new way to read in data, don't worry about it!

This dataset is pretty crazy! Take a look at it in the viewer, and then let’s do some data munging to get it into a nicer form.

-create a variable called injuries which indicates if the crash involved injuries or not. -create an unknown category for missing report_types -decide which other variables to keep

crash <- crash_raw %>%
  arrange(desc(crash_date)) %>%
  transmute(
    injuries = as.factor(if_else(injuries_total > 0, "injuries", "none")),
    injuries_total,
    crash_date = ymd_hms(crash_date),
    posted_speed_limit,
    weather_condition = as.factor(weather_condition), 
    lighting_condition = as.factor(lighting_condition),
    first_crash_type,
    prim_contributory_cause = as.factor(prim_contributory_cause),
    sec_contributory_cause,
    most_severe_injury = as.factor(most_severe_injury),
    crash_hour, crash_day_of_week, crash_month,
    latitude, longitude
  )

## Warning: There was 1 warning in `transmute()`.
## ℹ In argument: `crash_date = ymd_hms(crash_date)`.
## Caused by warning:
## !  747 failed to parse.

Exploratory Data Analysis

Here’s a few questions to get you started.

1. Take a look at crashes by latitude and longitude, colored by injuries. What do you notice?

crash %>%
  filter(latitude >0) %>%
  drop_na(injuries) %>%
  ggplot(aes(x=longitude, y=latitude, color=injuries)) +
  geom_point(size = 0.5, alpha = 0.4) +
  labs(color = NULL) +
  scale_color_manual(values = c("deeppink4", "gray80")) +
  coord_fixed()

2. What are the most common contributing factors to a crash?

crash %>%
  count(prim_contributory_cause) %>%
  arrange(desc(n)) %>%
  mutate(prim_contributory_cause = fct_reorder(prim_contributory_cause, n)) %>% 
  slice(2:4,6:7) %>%
  ggplot( aes(y = factor(prim_contributory_cause), x=n)) +
  geom_bar(stat="identity") +
  labs(title="Primary Causes of Crash, top 5", y="")

3. How do crashes vary month by month? Compare crashes by month in 2022 to 2023.

p <- crash %>%
  mutate(
    crash_year = as.factor(year(crash_date)),
    month_name = month(crash_month, label = TRUE, abbr = TRUE)
    ) %>%
  drop_na(crash_year) %>%
  filter(crash_year == 2022 | crash_year == 2023) %>%
  group_by(crash_year, month_name) %>%
  summarize(n = n()) %>%
  ggplot(aes(x=month_name, y = n, group=crash_year, color=crash_year)) +
  geom_line() + geom_point() +
  ylim(c(0,NA)) +
  labs(title="Crashes increased slightly in 2023",
       y="Number of Crashes", x="Month", color="Year")

## `summarise()` has grouped output by 'crash_year'. You can override using the
## `.groups` argument.

p

ggplotly(p)

4. Are crashes more likely to cause injuries when it is rainy and dark? Use the variables weather_condition and lighting_condition to explore.

p <- crash %>%
  drop_na(injuries) %>%
  filter(
    weather_condition != "UNKNOWN",
    lighting_condition != "UNKNOWN"
  ) %>%
  mutate(
    slippery = str_detect(weather_condition, 'RAIN|SNOW'),
    dark = str_detect(lighting_condition, 'DARK'),
    conditions = as.factor(case_when(
      (slippery & dark) ~ "Slippery and Dark Conditions", 
      (slippery & !dark) ~ "Slippery Only", 
      (!slippery & dark) ~ "Dark Only", 
      (!slippery & !dark) ~ "Not Slippery or Dark"
    )),
    conditions = fct_relevel(
      conditions,
      "Slippery and Dark Conditions", "Dark Only", "Slippery Only",  "Not Slippery or Dark"
      )
  ) %>%
  ggplot(aes(y=conditions, fill = injuries)) +
  geom_bar(position="dodge")

p

crash %>%
  drop_na(injuries) %>%
  filter(
    weather_condition != "UNKNOWN",
    lighting_condition != "UNKNOWN"
  ) %>%
  mutate(
    slippery = str_detect(weather_condition, 'RAIN|SNOW'),
    dark = str_detect(lighting_condition, 'DARK'),
    conditions = as.factor(case_when(
      (slippery & dark) ~ "Slippery and Dark Conditions", 
      (slippery & !dark) ~ "Slippery Only", 
      (!slippery & dark) ~ "Dark Only", 
      (!slippery & !dark) ~ "Not Slippery or Dark"
    )),
    conditions = fct_relevel(
      conditions,
      "Slippery and Dark Conditions", "Dark Only", "Slippery Only",  "Not Slippery or Dark"
      )
  ) %>%
  group_by(conditions, injuries) %>%
  reframe(n=n()) %>%
  group_by(conditions) %>%
  reframe(injuries = injuries, freq = n / sum(n)) %>%
  ggplot(aes(y=conditions, x=freq, fill = injuries)) +
  geom_bar(stat="identity") +
  scale_x_continuous(limits = c(0, 1), labels = percent_format()) +
  labs(x = "Relative Frequency", y="")

5. Choose another question you want to explore, and create an appropriate visual.

crash %>% 
  drop_na(injuries) %>%
  mutate(
    speed_limit = as.factor(if_else(posted_speed_limit <= 25, "Speed Limit <= 25", "Speed limit over 25"))) %>%
  group_by(speed_limit, injuries) %>%
  reframe(n=n()) %>%
  group_by(speed_limit) %>%
  reframe(injuries = injuries, freq = n / sum(n)) %>%
  ggplot(aes(x=speed_limit, y=freq, fill=injuries)) +
  geom_bar(stat="identity") +
  scale_y_continuous(limits = c(0, NA), labels = percent_format()) +
  labs(
    title="Injuries are less likely at speeds 25 mph or lower",
    x = "",
    y = "Relative Frequency")